Synthesis, Modification, and Application of Siloxane Materials for Environmental Sensing

As human populations and resource consumption increase, it is increasingly important to monitor the quality of our environment. While laboratory instruments offer useful information, portable, easy to use sensors would allow environmental analysis to occur on-site, at lower cost, and with minimal operator training. We explore the synthesis, modification, and applications of modified polysiloxane in environmental sensing. Multiple methods of producing modified siloxanes were investigated. Oligomers were formed by using functionalized monomers, producing siloxane materials containing silicon hydride, methyl, and phenyl side chains. Silicon hydride-functionalized oligomers were further modified by hydrosilylation to incorporate methyl ester and naphthyl side chains. Modifications to the siloxane materials were also carried out using post-curing treatments. Methyl ester-functionalized siloxane was incorporated into the surface of a cured poly(dimethylsiloxane) film by siloxane equilibration. The materials containing methyl esters were hydrolyzed to reveal carboxylic acids, which could later be used for covalent protein immobilization. Finally, the siloxane surfaces were modified to incorporate antibodies by covalent, affinity, and adsorption-based attachment. These modifications were characterized by a variety of methods, including contact angle, attenuated total reflectance Fourier transform infrared spectroscopy, dye labels, and 1H nuclear magnetic resonance spectroscopy. The modified siloxane materials were employed in a variety of sensing schemes. Volatile organic compounds were detected using methyl, phenyl, and naphthyl-functionalized materials on a Fabry-Perot interferometer and a refractometer. The Fabry-Perot interferometer was found to detect the analytes upon siloxane extraction by deformation of the Bragg reflectors. The refractometer was used to determine that naphthyl-functionalized siloxanes had elevated refractive indices, rendering these materials more sensitive to some analytes. Antibody-modified siloxanes were used to detect biological analytes through a solid phase microextraction-mediated enzyme linked immunosorbent assay (SPME ELISA). The SPME ELISA was found to have higher analyte sensitivity compared to a conventional ELISA system. The detection scheme was used to detect Escherichia coli at 8500 CFU/mL. These results demonstrate the variety of methods that can be used to modify siloxanes and the wide range of applications of modified siloxanes has been demonstrated through chemical and biological sensing schemes.

Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.